InBi-RRT: Incremental Bidirectional Tree Based Real-Time Path Planning/Replanning in Unknown Non-Convex Environments

Abstract

Real-time path planning in unknown non-convex environments is challenging, as obstacle updates can invalidate existing paths while narrow passages restrict feasible connectivity. This paper presents textbf{InBi-RRT}, an incremental bidirectional tree-based framework that grows a reverse tree from the goal and maintains a reusable forward tree from the start. When the current path becomes invalid, a cost-guided expansion selectively extends the forward tree to establish collision-free connections with the reverse tree, followed by backtracking and lightweight path optimization for efficient repair. Simulation results in unknown and non-convex scenarios demonstrate that InBi-RRT achieves significantly faster replanning than baseline methods, being up to textbf{5.5times} faster than RT-RRT and textbf{22times} faster than RRT^{text{X}}, with paths up to 19.8% shorter than RRT^{text{X}} under the same sample count. Furthermore, real-world experiments in an indoor maze-like environment verify the practicality and robustness of the proposed planner in unknown non-convex scenarios.